在keras中实现查看其训练loss值

loss_value= [ ]
self.history = model.fit(state,target_f,epochs=1, batch_size =32）
b = abs(float(self.history.history[‘loss'][0]))
loss_value.append(b)
print(loss_value)
loss_value = np.array( loss_value)
x = np.array(range(len( loss_value）））
plt.plot(x, loss_value, c = ‘g')
pt.svefit('c地址‘， dpi= 100)
plt.show()

 hist = model.fit_generator(generator=data_generator_reg(X=x_train, Y=[y_train_a,y_train_g], batch_size=batch_size),
         steps_per_epoch=train_num // batch_size,
         validation_data=(x_test, [y_test_a,y_test_g]),
         epochs=nb_epochs, verbose=1,
         workers=8, use_multiprocessing=True,
         callbacks=callbacks)

 logging.debug("Saving weights...")
 model.save_weights(os.path.join(db_name+"_models/"+save_name, save_name+'.h5'), overwrite=True)
 pd.DataFrame(hist.history).to_hdf(os.path.join(db_name+"_models/"+save_name, 'history_'+save_name+'.h5'), "history")

import pandas as pd
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np

def get_args():
 parser = argparse.ArgumentParser(description="This script shows training graph from history file.")
 parser.add_argument("--input", "-i", type=str, required=True,
      help="path to input history h5 file")
 args = parser.parse_args()
 return args

def main():
 args = get_args()
 input_path = args.input

 df = pd.read_hdf(input_path, "history")
 print(np.min(df['val_mean_absolute_error']))
 input_dir = os.path.dirname(input_path)
 plt.plot(df["loss"], '-o', label="loss (age)", linewidth=2.0)
 plt.plot(df["val_loss"], '-o', label="val_loss (age)", linewidth=2.0)
 plt.xlabel("Number of epochs", fontsize=20)
 plt.ylabel("Loss", fontsize=20)
 plt.legend()
 plt.grid()
 plt.savefig(os.path.join(input_dir, "loss.pdf"), bbox_inches='tight', pad_inches=0)
 plt.cla()

 plt.plot(df["mean_absolute_error"], '-o', label="training", linewidth=2.0)
 plt.plot(df["val_mean_absolute_error"], '-o', label="validation", linewidth=2.0)
 ax = plt.gca()
 ax.set_ylim([2,13])
 ax.set_aspect(0.6/ax.get_data_ratio())
 plt.xticks(fontsize=20)
 plt.yticks(fontsize=20)
 plt.xlabel("Number of epochs", fontsize=20)
 plt.ylabel("Mean absolute error", fontsize=20)
 plt.legend(fontsize=20)
 plt.grid()
 plt.savefig(os.path.join(input_dir, "performance.pdf"), bbox_inches='tight', pad_inches=0)

if __name__ == '__main__':
 main()